It is known within the art that road traffic, especially fully autonomously driven vehicles commonly known as self-driving vehicles, may benefit from deriving information from a database of events affecting the drivability, for instance on a lane level. Such a database may typically comprise information of traffic-affecting events on—or by the side of—the road, for instance relating to road debris, broken-down vehicles, slow-moving vehicles, etc., reported by and subsequently added to the database by different sources such as e.g., emergency authorities.
A database discussed above comprising up-to-date information may support tactical driving, for instance regarding choice of lanes and timing of lane changes. However, keeping the information related to the traffic-affecting events in the database updated, is challenging. For instance, the knowledge that a vehicle with detection capacity has travelled along a road where a traffic-affecting event has been recorded—without reporting the event again—may typically not be sufficient to conclude that the event is no longer relevant and that the event subsequently may be cleared from the database.
U.S. Pat. No. 9,047,773, for instance, relates to an exceptional road-condition warning system for a vehicle. The system may notice the driver and passenger in advance to respond to an exceptional road condition before the vehicle approaches the occurring place of the road condition through a back-end cooperative self-learning mechanism. However, although the back-end cooperative self-learning mechanism disclosed in U.S. Pat. No. 9,047,773 may collect the exceptional road condition from different vehicles and update the database automatically to maintain the accuracy, keeping a database of traffic-affecting events up-to-date, remains a challenge.